MarvelRivalsData
Jods
2025-02-03
ranks <- read.csv("data/historical/ranks_historical.csv")
# Define a custom color palette for each rank
rank_colors <- c(
"bronze" = "#cd7f32",
"bronze - 1" = "#cd7f32",
"bronze - 2" = "#cd7f32",
"bronze - 3" = "#cd7f32",
"silver" = "#c0c0c0",
"silver - 1" = "#c0c0c0",
"silver - 2" = "#c0c0c0",
"silver - 3" = "#c0c0c0",
"gold" = "#ffd700",
"gold - 1" = "#ffd700",
"gold - 2" = "#ffd700",
"gold - 3" = "#ffd700",
"platinum" = "#e5e4e2",
"platinum - 1" = "#e5e4e2",
"platinum - 2" = "#e5e4e2",
"platinum - 3" = "#e5e4e2",
"diamond" = "#b9f2ff",
"diamond - 1" = "#b9f2ff",
"diamond - 2" = "#b9f2ff",
"diamond - 3" = "#b9f2ff",
"grandmaster" = "#d3af37",
"grandmaster - 1" = "#d3af37",
"grandmaster - 2" = "#d3af37",
"grandmaster - 3" = "#d3af37",
"celestial" = "#8a2be2",
"celestial - 1" = "#8a2be2",
"celestial - 2" = "#8a2be2",
"celestial - 3" = "#8a2be2",
"eternity - total" = "#ff1493",
"one_above_all - total" = "#ff4500",
"eternity" = "#ff1493",
"one_above_all" = "#ff4500"
)
custom_rank_order <- c( "bronze",
"bronze - 3",
"bronze - 2",
"bronze - 1",
"silver",
"silver - 3",
"silver - 2",
"silver - 1",
"gold",
"gold - 3",
"gold - 2",
"gold - 1",
"platinum",
"platinum - 3",
"platinum - 2",
"platinum - 1",
"diamond",
"diamond - 3",
"diamond - 2",
"diamond - 1",
"grandmaster",
"grandmaster - 3",
"grandmaster - 2",
"grandmaster - 1",
"celestial",
"celestial - 3",
"celestial - 2",
"celestial - 1",
"eternity - total",
"one_above_all - total")
latest_rank_data <- fromJSON("data/latest/latest_ranks.json")
ranks <- ranks %>%
mutate(timestamp = ymd_hms(timestamp)) %>%
arrange(timestamp) # Sort by timestamp
# Convert timestamp column to a proper datetime format
ranks$timestamp <- ymd_hms(ranks$timestamp)
# Shorten timestamps for readability (e.g., removing seconds)
ranks$short_timestamp <- format(ranks$timestamp, "%Y-%m-%d %H:%M")
# Create a new column combining rank + division to distinguish lines
ranks <- ranks %>%
mutate(rank_division = paste(rank, division, sep = " - "))
ranks$rank_division <- factor(ranks$rank_division, levels = custom_rank_order)
ranks_total <- ranks %>%
group_by(timestamp, rank) %>%
summarise(total_population = sum(population_count), .groups = "drop")
ranks_total$rank <- factor(ranks_total$rank, levels = custom_rank_order)# Create the plot
fig <- plot_ly(ranks,
x = ~timestamp,
y = ~population_count,
color = ~rank_division, # Differentiate by rank + division
colors = rank_colors,
type = 'scatter',
mode = 'lines+markers',
text = ~paste("Rank:", rank, "<br>Division:", division, "<br>Count:", population_count),
hoverinfo = "text")
fig <- fig %>%
layout(title = "Population Count Over Time (by Division)",
xaxis = list(title = "Timestamp", tickangle = -45),
yaxis = list(title = "Population Count"),
legend = list(title = list(text = "Rank - Division")))
figfig_total <- plot_ly(ranks_total,
x = ~timestamp,
y = ~total_population,
color = ~rank,
colors = rank_colors,
type = 'scatter',
mode = 'lines+markers',
text = ~paste("Rank:", rank, "<br>Total Count:", total_population),
hoverinfo = "text")
fig_total <- fig_total %>%
layout(title = "Total Population Count Over Time (Summed by Rank)",
xaxis = list(title = "Timestamp", tickangle = -45),
yaxis = list(title = "Total Population Count"),
legend = list(title = list(text = "Rank")))
fig_total# Example dataset (replace with your actual data)
rank_population <- ranks %>%
group_by(rank_division) %>%
slice_max(timestamp, with_ties = FALSE) %>%
rename(population = population_count) %>%
select(-timestamp, -short_timestamp) %>%
ungroup()
# Total players (including Bronze 3)
total_players_including <- sum(rank_population$population)
# Total players (excluding Bronze 3)
total_players_excluding <- sum(rank_population$population[rank_population$rank_division != "bronze - 3"])
# Compute percentages
rank_population <- rank_population %>%
mutate(
percent_of_players_in_rank = round((population / total_players_including) * 100,2),
percent_of_players_in_rank_excluding = if_else(rank_division == "bronze - 3", 0,round((population / total_players_excluding) * 100,2)),
)
rank_population <- rank_population %>%
mutate(
percent_below_rank_including = round(cumsum(percent_of_players_in_rank)-percent_of_players_in_rank,2), # Reverse cumulative sum
percent_below_rank_excluding = round(cumsum(percent_of_players_in_rank_excluding)-percent_of_players_in_rank_excluding,2),
) get_rank_image <- function(rank_division) {
# Split the rank_division (e.g., "bronze - 1" to "bronze" and "1")
split_rank <- strsplit(rank_division, " - ")
rank <- split_rank[[1]][1] # Get the rank (e.g., "bronze")
# Return the corresponding image URL from the JSON data
return(latest_rank_data[[rank]]$image)
}
# Create a numeric rank system for easier calculations
rank_to_numeric <- c("bronze - 3" = 1, "bronze - 2" = 2, "bronze - 1" = 3,
"silver - 3" = 4, "silver - 2" = 5, "silver - 1" = 6,
"gold - 3" = 7, "gold - 2" = 8, "gold - 1" = 9,
"platinum - 3" = 10, "platinum - 2" = 11, "platinum - 1" = 12,
"diamond - 3" = 13, "diamond - 2" = 14, "diamond - 1" = 15,
"grandmaster - 3" = 16, "grandmaster - 2" = 17, "grandmaster - 1" = 18,
"celestial - 3" = 19, "celestial - 2" = 20, "celestial - 1" = 21,
"eternity - total" = 22, "one_above_all - total" = 23)
# Apply the numeric rank conversion
rank_population <- rank_population %>%
mutate(rank_numeric = rank_to_numeric[as.character(rank_division)])
# Weighted average rank (including unranked players)
average_rank_including <- sum(rank_population$rank_numeric * rank_population$population) / total_players_including
# Weighted average rank (excluding unranked players)
rank_population_excluding_unranked <- rank_population %>%
filter(rank_division != "bronze - 3")
average_rank_excluding <- sum(rank_population_excluding_unranked$rank_numeric * rank_population_excluding_unranked$population) / total_players_excluding
# Convert numeric ranks back to rank names
numeric_to_rank <- names(rank_to_numeric)
average_rank_including_name <- numeric_to_rank[floor(average_rank_including)]
average_rank_including_points <- round((average_rank_including - floor(average_rank_including))*100)
average_rank_excluding_name <- numeric_to_rank[floor(average_rank_excluding)]
average_rank_excluding_points <- round((average_rank_excluding - floor(average_rank_excluding))*100)
rank_image <- get_rank_image(average_rank_including_name)
rank_image_ex <- get_rank_image(average_rank_excluding_name)
# Print the resultsAverage Rank
silver - 3 | 85 lp (including bronze 3/unranked)
silver - 1 | 47 lp (excluding bronze 3/unranked)
rank_population$rank <- sapply(rank_population$rank, get_rank_image)
# Convert image URLs to HTML <img> tags
rank_population$rank <- paste0('<img src="', rank_population$rank, '" height="30px">')
rank_population <- rank_population %>%
select(-percent_of_players_in_rank_excluding) %>%
rename("% in Rank" = percent_of_players_in_rank,
"% below (with unranked)" = percent_below_rank_including,
"% below (w/o unranked)" = percent_below_rank_excluding
)
# Print table
datatable(rank_population, escape = FALSE,options = list(
pageLength = 25, autoWidth = TRUE,
columnDefs = list(
list(targets = c(4, 8), visible = FALSE) # Adjust these numbers based on step 1
)
)
) %>%
formatStyle(
columns = names(rank_population), # Apply to all columns
`text-align` = "center" # Center-align text
) Created by